A spike-based adaptive filter

Author

Gong, Xiaoxiang ; Harris, John G.

Author_Institution

Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA

fYear

2004

fDate

13-15 Dec. 2004

Firstpage

322

Lastpage

325

Abstract

We propose a spike-based adaptive filter with supervised learning. Unlike standard adaptive filters, here the optimal MSE solution is not unique for the spike-based system identification problem. The simplex method is introduced to select one of the many possible optimal solutions. In simulations, an LMS-based learning procedure is designed and, for faster convergence, we introduce a credit assignment method which penalizes all the weights contributing to the current error signal. Finally, we discuss issues regarding the implementation of the spike-based adaptive filter in an analog VLSI circuit.

Keywords

VLSI; adaptive filters; analogue integrated circuits; convergence; integrating circuits; learning (artificial intelligence); least mean squares methods; linear programming; pulse circuits; LMS-based learning procedure; MSE; analog VLSI implementation; convergence; credit assignment method; integrate-and-fire mechanism; linear programming; simplex method; spike timing; spike-based adaptive filter; supervised learning; system identification; Adaptive filters; Biological information theory; Circuit simulation; Laboratories; Neural engineering; Signal design; Supervised learning; System identification; Timing; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 2004. ICECS 2004. Proceedings of the 2004 11th IEEE International Conference on

Print_ISBN

0-7803-8715-5

Type

conf

DOI

10.1109/ICECS.2004.1399683

Filename

1399683